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---
dataset_info:
features:
- name: sms
dtype: string
- name: label
dtype: int64
- name: char_len
dtype: int64
- name: word_count
dtype: int64
- name: punct_score
dtype: int64
- name: spam_keywords
dtype: int64
- name: lexical_diversity
dtype: float64
- name: readability
dtype: float64
- name: caps_ratio
dtype: float64
- name: digit_ratio
dtype: float64
- name: exclaim_ratio
dtype: float64
- name: url_flag
dtype: int64
- name: spammy_words
dtype: int64
- name: entropy
dtype: float64
splits:
- name: train
num_bytes: 974514
num_examples: 5171
download_size: 446788
dataset_size: 974514
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# SMS Spam Enriched Dataset
An enriched version of the classic **SMS Spam Collection Dataset from UC Irvine** with additional engineered features and semantic embeddings.
This dataset is designed for **spam detection, feature engineering experiments, and model interpretability research**.
---
## Dataset Overview
- **Total samples**: 5,171
- **Classes**:
- `0`: Ham (non-spam)
- `1`: Spam
---
## Enrichments Added
Alongside the raw SMS text (`sms`) and labels (`label`), we engineered multiple new features:
1. **char_len** → Total characters in the message
2. **word_count** → Total words in the message
3. **punct_score** → Weighted score for punctuation usage (`!`, `?`, `...`)
4. **spam_keywords** → Count of known spammy tokens (`free`, `win`, `urgent`, etc.)
5. **lexical_diversity** → Ratio of unique words to total words
6. **readability** → Flesch Reading Ease score
7. **caps_ratio** → Proportion of uppercase characters
8. **digit_ratio** → Proportion of numeric digits
9. **exclaim_ratio** → Ratio of exclamation marks to total characters
10. **url_flag** → Binary indicator for presence of URLs
11. **spammy_words** → Count of flagged high-signal words
12. **entropy** → Shannon entropy of characters
13. **embeddings** → Sentence-transformer vector representations (for downstream tasks like clustering, semantic similarity, visualization)
---
## Example Row
| sms | label | char_len | word_count | punct_score | spam_keywords | lexical_diversity | readability | caps_ratio | digit_ratio | url_flag | entropy |
|------------------------------------------------|-------|----------|------------|-------------|---------------|-------------------|-------------|------------|-------------|----------|---------|
| "Free entry in 2 a wkly comp to win FA Cup..." | 1 | 156 | 28 | 0 | 3 | 0.857 | 80.83 | 0.064 | 0.16 | 0 | 4.69 |
---
## Visualizations
Below is a **PCA projection** of SMS embeddings, showing clear separation between spam (red) and ham (blue):
---
## Benchmark Models
We trained baseline classifiers using the enriched dataset:
- **Logistic Regression (with combined features)**
- Accuracy: ~99%
- F1 (spam): ~0.95
- **Random Forest (with combined features)**
- Accuracy: ~98%
- F1 (spam): ~0.92
Logistic Regression Report (Combined Features):
precision recall f1-score support
0 0.99 1.00 0.99 904
1 0.98 0.92 0.95 131
accuracy 0.99 1035
macro avg 0.99 0.96 0.97 1035
weighted avg 0.99 0.99 0.99 1035
Random Forest Report (Combined Features):
precision recall f1-score support
0 0.98 1.00 0.99 904
1 0.99 0.86 0.92 131
accuracy 0.98 1035
macro avg 0.99 0.93 0.96 1035
weighted avg 0.98 0.98 0.98 1035
---
## Use Cases
- Spam detection model training
- Feature engineering demonstration
- Embedding-based similarity and clustering tasks
- Educational material for NLP + ML pipelines
---
## Citation
If you use this dataset, please cite the original SMS Spam Collection dataset:
> @inproceedings{Almeida2011SpamFiltering, title={Contributions to the Study of SMS Spam Filtering: New Collection and Results}, author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami}, year={2011}, booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)", }.
> Dataset Enrichment and Feature Engineering contributions by Naga Adithya Kaushik (GenAIDevTOProd).
---
## License
This dataset is distributed under the same terms as the original SMS Spam dataset (publicly available for research).
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